Data storage and anti-crawling strategies in Python crawlers

In Python crawler development, we often face two key issues: how to effectively store the data obtained by the crawler, and how to deal with the website's anti-crawler strategy. This article will elaborate on these two issues for you through question and answer methods and provide corresponding solutions.
Question 1: How to effectively store crawled data?
Data storage is a part of the database in crawler development. We can choose to store the data in a database or save it as a local file. If you choose to store in a database, we need to install the corresponding database library, such as MySQLdb or pymysql. We can then create a database connection and create a table to store the data. In the crawler code, we can insert the crawled data into the database. Another common way to store data is to save data as local files. In the crawler code, we can use file operations to read data into local files. So what is the implementation process of data storage? The following two are common ways of storing data:

1. Store to database:
   • First, we need to install database-related Python libraries, such as MySQLdb, pymysql, etc.
   • Then, create a database connection and create a corresponding table to store data.
   • In the crawler code, insert the crawled data into the database.

The sample code is as follows:

   import pymysql

   # 创建数据库连接
   conn = pymysql.connect(host='localhost', user='root', password='password', database='mydb')
   cursor = conn.cursor()

   # 创建表格
   cursor.execute("CREATE TABLE IF NOT EXISTS data (id INT AUTO_INCREMENT PRIMARY KEY, title VARCHAR(255), content TEXT)")

   # 插入数据
   title = 'Python爬虫'
   content = '这是一篇关于Python爬虫的文章'
   cursor.execute("INSERT INTO data (title, content) VALUES (%s, %s)", (title, content))

   # 提交事务并关闭连接
   conn.commit()
   cursor.close()
   conn.close()

2. Save as local file:

• In the crawler code, write the crawled data to a local file.

The sample code is as follows:

   with open('data.txt', 'a', encoding='utf-8') as f:
       title = 'Python爬虫'
       content = '这是一篇关于Python爬虫的文章'
       f.write(f'Title: {title}\nContent: {content}\n')

Question 2: How to deal with the anti-crawler strategy of the website?
During the crawling process, we also need an anti-crawling strategy for the website. Websites may take some measures to prevent crawlers, such as IP bans and verification code bans. In order to circumvent IP blocking, we can use hidden proxy IP to the real IP address. By using a third party library like requests, we can set the proxy IP to send requests. When it comes to CAPTCHAs, the handling varies from site to site. A common approach is to use image processing libraries, such as PIL and pytesseract, to recognize the verification code and submit it automatically. This can bypass the manual input step of the verification code and improve the efficiency of the crawler. The two implementation processes are as follows:

1. Use proxy IP:
   • By using proxy IP, we hide the real IP address and thus can circumvent IP blocking.
   • In Python crawler, we can use third-party libraries such as requests to set the proxy IP.

The sample code is as follows:

   import requests

   proxyHost = "u6205.5.tp.16yun.cn"
   proxyPort = "5445"
   proxyUser = "16QMSOML"
   proxyPass = "280651"

   proxies = {
       "http": f"http://{proxyUser}:{proxyPass}@{proxyHost}:{proxyPort}",
       "https": f"https://{proxyUser}:{proxyPass}@{proxyHost}:{proxyPort}"
   }

   response = requests.get(url, proxies=proxies)

1. Process verification code:
   • When encountering a situation where a verification code needs to be entered, we can use a third-party library (such as pytesseract) to automatically identify the verification code and process it accordingly.

The sample code is as follows:

   import pytesseract
   from PIL import Image

   # 下载验证码图片并保存为image.png
   # ...

   # 识别验证码
   image = Image.open('image.png')
   code = pytesseract.image_to_string(image)

   # 提交验证码并继续爬取
   # ...

Data storage and anti-crawling strategies in Python crawlers are issues that need to be focused on during crawler development. By choosing the appropriate data storage method and anti-crawling strategy, we can better complete the crawling task and obtain the data we need. In actual development, we choose appropriate solutions based on specific situations and flexibly respond to different website anti-crawler strategies. In this way, we can successfully crawl data and overcome the limitations of the website to achieve our crawling goals.